R Notebook

#install.packages("knitr")
#install.packages("grid")
install.packages("plotly")

Error in install.packages : Updating loaded packages

#install.packages("dprep")
#install.packages("normalr")
#install.packages("ggcorrplot")

#install.packages("RColorBrewer")
#install.packages("rgdal")
#install.packages("jsonlite")
#install.packages("readr")

library(gridExtra)
library(dplyr)
library(lubridate)
library(magrittr)
library(ggplot2)
library(tidyr)
library(knitr)
library(normalr)
library(ggcorrplot)

library(leaflet)
#library(plotly)
library(RColorBrewer)

package 㤼㸱RColorBrewer㤼㸲 was built under R version 4.0.3

library(readr)

package 㤼㸱readr㤼㸲 was built under R version 4.0.3

#library(MLRMPA)
#??src_mysql
my_db <- src_mysql(
  dbname = "coronavirus",
  host = "localhost",
  user = "root",
  password = "1234"
)

`src_mysql()` is deprecated as of dplyr 1.0.0.
Please use `tbl()` directly with a database connection
This warning is displayed once every 8 hours.
Call `lifecycle::last_warnings()` to see where this warning was generated.

my_db

src:  mysql 8.0.21 [root@localhost:/coronavirus]
tbls: avg_world_temp_2020, covid_thailand, covid_us, covid19_confirmed, covid19_deaths,
  covid19_recovered, covidus, gdp, gdp19, healthranking, population, pornhub, sars,
  sars_2003, sars_2003_update, top20pornhub

##import data
df_conf <- tbl(my_db, sql("select * from covid19_confirmed "))
df_conf <- as.data.frame(df_conf)
df_conf

df_deaths <- tbl(my_db, sql("select * from covid19_deaths "))
df_deaths <- as.data.frame(df_deaths)
df_deaths

df_recover <- tbl(my_db, sql("select * from covid19_recovered "))
df_recover <- as.data.frame(df_recover)
df_recover

##check the time frame of the data
n.col <- ncol(df_conf)
dates <- names(df_conf)[5:n.col]%>% mdy()
range(dates)

[1] "2020-01-22" "2021-01-14"

min.date <- min(dates)
max.date <- max(dates)
min.date.txt <- min.date %>% format('%d %b %Y')
max.date.txt <- max.date %>% format('%d %b %Y')

#clean data
cleanData <- function(data) {
  ## remove some columns
  data %<>% select(-c(Province.State, Lat, Long)) %>% rename(country=Country.Region)
  ## convert from wide to long format
  data %<>% gather(key=date, value=count, -country)
  ## convert from character to date
  data %<>% mutate(date = date %>% mdy())
  ## aggregate by country
  data %<>% group_by(country, date) %>% summarise(count=sum(count, na.rm=T)) %>% as.data.frame()
  return(data)
}
## clean the three data sets
data.confirmed <- df_conf %>% cleanData() %>% rename(confirmed=count)

`summarise()` regrouping output by 'country' (override with `.groups` argument)

data.deaths <- df_deaths %>% cleanData() %>% rename(deaths=count)

`summarise()` regrouping output by 'country' (override with `.groups` argument)

data.recovered <- df_recover %>% cleanData() %>% rename(recovered=count)

`summarise()` regrouping output by 'country' (override with `.groups` argument)

data <- data.confirmed %>% merge(data.deaths, all=T) %>% merge(data.recovered, all=T)
data

## countries/regions with confirmed cases, excl. cruise ships
countries <- data %>% pull(country) %>% setdiff('Cruise Ship')
data 

data.world <- data %>% group_by(date) %>%
  summarise(country='World',
            confirmed = sum(confirmed, na.rm=T),
            deaths = sum(deaths, na.rm=T),
            recovered = sum(recovered, na.rm=T))

`summarise()` ungrouping output (override with `.groups` argument)

data %<>% rbind(data.world)
data

data %<>% mutate(current.confirmed = confirmed - deaths - recovered)
data

NA

#rate
data %<>% arrange(country, date)
n <- nrow(data)
day1 <- min(data$date)
data %<>% mutate(new.confirmed = ifelse(date == day1, NA, confirmed - lag(confirmed, n=1)),
                 new.deaths = ifelse(date == day1, NA, deaths - lag(deaths, n=1)),
                 new.recovered = ifelse(date == day1, NA, recovered - lag(recovered, n=1)))
data %<>% mutate(new.confirmed = ifelse(new.confirmed < 0, 0, new.confirmed),
                 new.deaths = ifelse(new.deaths < 0, 0, new.deaths),
                 new.recovered = ifelse(new.recovered < 0, 0, new.recovered))
## death rate based on total deaths and recovered cases
data %<>% mutate(rate.upper = (100 * deaths / (deaths + recovered)) %>% round(1))
## lower bound: death rate based on total confirmed cases
data %<>% mutate(rate.lower = (100 * deaths / confirmed) %>% round(1))
## death rate based on the number of death/recovered on every single day
data %<>% mutate(rate.daily = (100 * new.deaths / (new.deaths + new.recovered)) %>% round(1))
View(data)

## convert from wide to long format
data.long <- data %>%
  select(c(country, date, confirmed, current.confirmed, recovered, deaths)) %>%
  gather(key=type, value=count, -c(country, date))
## set factor levels to show them in a desirable order
data.long %<>% mutate(type=recode_factor(type, confirmed='Total Confirmed',
                                         current.confirmed='Current Confirmed',
                                         recovered='Recovered',
                                         deaths='Deaths'))
#View(data.long)

##Number of case World
world <- filter(data.long,country == 'World')
plot1 <- world %>% filter(type != 'Total Confirmed') %>%
  ggplot(aes(x=date, y=count)) +
  geom_area(aes(fill=type), alpha=0.5) +
  labs(title=paste0('Numbers of Cases Worldwide - ', max.date.txt)) +
  scale_fill_manual(values=c('red', 'green', 'black')) +
  theme(legend.title=element_blank(), legend.position='bottom',
        plot.title = element_text(size=7),
        axis.title.x=element_blank(),
        axis.title.y=element_blank(),
        legend.key.size=unit(0.2, 'cm'),
        legend.text=element_text(size=6),
        axis.text=element_text(size=7),
        axis.text.x=element_text(angle=45, hjust=1))
plot2 <- world %>%
  ggplot(aes(x=date, y=count)) +
  geom_line(aes(color=type)) +
  labs(title=paste0('Numbers of Cases Worldwide (log scale) - ', max.date.txt)) +
  scale_color_manual(values=c('purple', 'red', 'green', 'black')) +
  theme(legend.title=element_blank(), legend.position='bottom',
        plot.title = element_text(size=14),
        axis.title.x=element_blank(),
        axis.title.y=element_blank(),
        legend.key.size=unit(0.2, 'cm'),
        legend.text=element_text(size=14),
        axis.text=element_text(size=14),
        axis.text.x=element_text(angle=45, hjust=1)) +
  scale_y_continuous(trans='log10')
## show two plots side by side
grid.arrange(plot1, plot2, ncol=2)

plot2

## Current Confirmed Cases
data.world <- data %>% filter(country=='World')
n <- nrow(data.world)
plot1 <- ggplot(data.world, aes(x=date, y=current.confirmed)) +
  geom_point() + geom_smooth() +
  xlab('') + ylab('Count') + labs(title='Current Confirmed Cases') +
  theme(axis.text.x=element_text(angle=45, hjust=1))
plot2 <- ggplot(data.world, aes(x=date, y=new.confirmed)) +
  geom_point() + geom_smooth() +
  xlab('') + ylab('Count') + labs(title='Daily New Confirmed Cases') +
  theme(axis.text.x=element_text(angle=45, hjust=1))
## show two plots side by side
grid.arrange(plot1, plot2, ncol=2)

View(data.world)

## a scatter plot with a smoothed line and vertical x-axis labels
plot1 <- ggplot(data.world, aes(x=date, y=deaths)) +
  geom_point() + geom_smooth() +
  xlab('') + ylab('Count') + labs(title='Accumulative Deaths') +
  theme(axis.text.x=element_text(angle=45, hjust=1))
plot2 <- ggplot(data.world, aes(x=date, y=recovered)) +
  geom_point() + geom_smooth() +
  xlab('') + ylab('Count') + labs(title='Accumulative Recovered Cases') +
  theme(axis.text.x=element_text(angle=45, hjust=1))
plot3 <- ggplot(data.world, aes(x=date, y=new.deaths)) +
  geom_point() + geom_smooth() +
  xlab('') + ylab('Count') + labs(title='New Deaths') +
  theme(axis.text.x=element_text(angle=45, hjust=1))
plot4 <- ggplot(data.world, aes(x=date, y=new.recovered)) +
  geom_point() + geom_smooth() +
  xlab('') + ylab('Count') + labs(title='New Recovered Cases') +
  theme(axis.text.x=element_text(angle=45, hjust=1))
## show four plots together, with 2 plots in each row
grid.arrange(plot1, plot2, plot3, plot4, nrow=2)

## convert from wide to long format, for drawing area plots
rates.long <- data %>%
  select(c(country, date, rate.upper, rate.lower, rate.daily)) %>%
  gather(key=type, value=count, -c(country, date))
# set factor levels to show them in a desirable order
rates.long %<>% mutate(type=recode_factor(type, rate.daily='Daily',
                             rate.upper='Upper bound'))

## ranking by confirmed cases
data.latest.all <- data %>% filter(date == max(date)) %>%
  select(country, date,confirmed, new.confirmed, current.confirmed,
         recovered, deaths, new.deaths, death.rate=rate.lower) %>%
  mutate(ranking = dense_rank(desc(confirmed)))
#View(data.latest.all)
k <- 20
## top 20 countries: 21 incl. 'World'
top.countries <- data.latest.all %>% filter(ranking <= k + 1) %>%
  arrange(ranking) %>% pull(country) %>% as.character()
top.countries %>% setdiff('World') %>% print()

 [1] "US"             "India"          "Brazil"         "Russia"         "United Kingdom"
 [6] "France"         "Turkey"         "Italy"          "Spain"          "Germany"       
[11] "Colombia"       "Argentina"      "Mexico"         "Poland"         "Iran"          
[16] "South Africa"   "Ukraine"        "Peru"           "Netherlands"    "Indonesia"     

data.latest <- data.latest.all %>% filter(!is.na(country)) %>%
  mutate(country=ifelse(ranking <= k + 1, as.character(country), 'Others')) %>%
  mutate(country=country %>% factor(levels=c(top.countries, 'Others')))
data.latest %<>% group_by(country) %>%
  summarise(confirmed=sum(confirmed), new.confirmed=sum(new.confirmed),
            current.confirmed=sum(current.confirmed),
            recovered=sum(recovered), deaths=sum(deaths), new.deaths=sum(new.deaths)) %>%
  mutate(death.rate=(100 * deaths/confirmed) %>% round(1)) 

`summarise()` ungrouping output (override with `.groups` argument)

data.latest

data.latest %<>% select(c(country, confirmed, deaths, death.rate,
                          new.confirmed, new.deaths, current.confirmed,recovered)) %>%
  mutate(recover.rate=(100 * recovered/confirmed) %>% round(1))
data.latest

df_pop <- tbl(my_db, sql("select * from population "))
df_pop <- as.data.frame(df_pop)
df_pop <- rename(df_pop,"country"="Country")
df_pop

data.latest <- merge(x = data.latest, y = df_pop, by = "country", all.x = TRUE) 
data.latest <- rename(data.latest,"population" = "Population (2020)")
data.latest

data.latest  %<>% select(c(country, confirmed, deaths, death.rate,
                          new.confirmed, new.deaths,
                          current.confirmed,recovered,recover.rate,population)) %>%
  mutate(confirm.rate=(100 *confirmed/population) %>% round(1))
data.latest

NA

data.latest %>% mutate(death.rate=death.rate %>% format(nsmall=1) %>% paste0('%'))

NA
NA

## convert from wide to long format, for drawing area plots
data.latest.long <- data.latest %>% filter(country!='World') %>%
  gather(key=type, value=count, -country)
## set factor levels to show them with proper text and in a desirable order
data.latest.long %<>% mutate(type=recode_factor(type,
                                                confirmed='Total Confirmed',
                                                deaths='Total Deaths',
                                                death.rate='Death Rate (%)',
                                                new.confirmed='New Confirmed (compared with one day before)',
                                                new.deaths='New Deaths (compared with one day before)',
                                                current.confirmed='Current Confirmed',
                                                recover.rate = 'Recover Rate(%)',
                                                confirm.rate = 'Confirmed Rate(%)'))
#View(data.latest.long)
data.one.dem <- filter(data.latest.long,type=='Total Confirmed'
                       | type=='Total Deaths'
                       | type=='Current Confirmed')
data.two.dem <- filter(data.latest.long,type=='Death Rate (%)'
                       | type=='New Confirmed (compared with one day before)'
                       | type=='New Deaths (compared with one day before)'
                       | type=='Recover Rate(%)'
                       | type=='Confirmed Rate(%)')
data.two.dem

## bar chart
data.one.dem %>% ggplot(aes(x=country, y=count, fill=country, group=country)) +
  geom_bar(stat='identity') +
  geom_text(aes(label=count, y=count), size=2, vjust=0) +
  xlab('') + ylab('') +
  labs(title=paste0('Top 20 Countries with Most Confirmed Cases - ', max.date.txt)) +
  scale_fill_discrete(name='Country', labels=aes(count)) +
  theme(legend.title=element_blank(),
        legend.position='none',
        plot.title=element_text(size=11),
        axis.text=element_text(size=7),
        axis.text.x=element_text(angle=45, hjust=1)) +
  facet_wrap(~type, ncol=1, scales='free_y')

data.two.dem %>% ggplot(aes(x=country, y=count, fill=country, group=country)) +
  geom_bar(stat='identity') +
  geom_text(aes(label=count, y=count), size=2, vjust=0) +
  xlab('') + ylab('') +
  labs(title=paste0('Top 20 Countries with Most Confirmed Cases - ', max.date.txt)) +
  scale_fill_discrete(name='Country', labels=aes(count)) +
  theme(legend.title=element_blank(),
        legend.position='none',
        plot.title=element_text(size=11),
        axis.text=element_text(size=7),
        axis.text.x=element_text(angle=45, hjust=1)) +
  facet_wrap(~type, ncol=1, scales='free_y')

##GDP
df_gdp <- tbl(my_db, sql("select * from gdp"))
df_gdp <- as.data.frame(df_gdp)
df_gdp <- rename(df_gdp,"country"="Real GDP growth (Annual percent change)")
df_gdp <- select(df_gdp,c("country","2012","2013","2014","2015","2016","2017","2018","2019","2020","2021"))
df_gdp

df_gdp2019 <- tbl(my_db, sql("select * from gdp19"))
df_gdp2019 <- as.data.frame(df_gdp2019)
df_gdp2019

NA

#healthranking
df_healt <- tbl(my_db, sql("select * from healthranking"))
df_healt <- as.data.frame(df_healt)
df_healt <- select(df_healt,c("country","healthCareIndex"))
df_healt

#Top20Pornhub
df_pornhub <- tbl(my_db, sql("select * from Pornhub"))
df_pornhub <- as.data.frame(df_pornhub)
df_pornhub

NA

#temp
df_temp <- tbl(my_db, sql("select * from Avg_World_Temp_2020"))
df_temp <- as.data.frame(df_temp)
df_city <- select(df_temp,c("Country","City")) %>%
  rename(country=Country) %>% 
  rename(city=City)
numofcity <- aggregate(city ~ country, data = df_city, length)
df_temp <- select(df_temp,c("Country","Apr","May","Jun","Jul","Aug")) %>%
  rename(country=Country)
df_temp <- data.frame(country=df_temp[,1],avg=rowMeans(df_temp[,-1]))
df_temp <- df_temp %<>% group_by(country) %>% summarise(avg_temp = mean(avg,na.rm = TRUE))

`summarise()` ungrouping output (override with `.groups` argument)

df_temp <- df_temp %>% mutate(country=ifelse(country=="United States","US", country ) ) 
df_temp$avg_temp <- df_temp$avg_temp %>% 
  sprintf(df_temp$avg_temp, fmt = '%#.1f') %>%
  as.numeric(df_temp$avg_temp)
df_temp

#Top 20 with gdp
data.longGDP <- df_gdp %>% gather(key=year, value=GDP, -c(country))
data.top <- data.latest %>% filter(country!='World')
data.top <- head(data.top,20)
#View(data.top)
data.gdp <- filter(data.longGDP,year=='2020')
#View(data.gdp)
#merge
mergcountry = function(data1,data2){
  data <- merge(x = data1, y = data2, by = "country", all.x = TRUE) 
  return(data)
}
data.top.world <- merge(x = data.top, y = df_gdp2019, by = "country", all.x = TRUE) %>% 
  select(-c(code,rank,new.confirmed,new.deaths,current.confirmed,population)) %>% 
  rename(GDP="GDP (millions of US dollars)")

data.top.world <- merge(x = data.top.world, y = df_healt, by = "country", all.x = TRUE) %>%
  rename(healthcare="healthCareIndex")
#data.top.world <- mergcountry(data.top.world, df_temp)

data.top.world <- merge(x = data.top.world, y = df_pornhub, by = "country", all.x = TRUE) %>%
  rename(Pornhub = "PornhubIndex(%)")

data.top.world <- mergcountry(data.top.world, df_temp)
index <- is.na(data.top.world)
data.top.world[index] <- 0
data.top.world

#View(data.top.world)

normalize = function(data){
  #return ((data - min(data,na.rm = TRUE))/(max(data,na.rm = TRUE) - min(data,na.rm = TRUE)))
  z <- scale(data);
  tanh(z/2)
}
norm_data = as.data.frame(apply(data.top.world[,2:12],2,normalize))
corr_data <- norm_data
norm_data$country <- c("Argentina","Bangladesh","Brazil","Chile","Colombia","France","Germany","India","Iran","Italy","Mexico","Pakistan","Peru","Russia","saudi Arabia","South Africa","Spain","Turkey","United Kingdom","US")
#View(norm_data)


norm_data_plot <- select(norm_data,"country","confirm.rate","death.rate","recover.rate","healthcare","Pornhub","GDP","avg_temp")
norm_data_plot %<>% gather(key=type, value=count, -c(country))
level_order <- factor(norm_data_plot$type, 
                      level = c("GDP","avg_temp","healthcare","recover.rate","death.rate","confirm.rate","Pornhub"))
ggplot(data = norm_data_plot, aes(x=country, y=level_order, fill=count)) + 
  geom_tile() +
  scale_fill_gradient(low = "pink", high = "blue") +
  xlab("") +
  ylab("") +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 90,vjust = 1))+
  theme(
    axis.line = element_blank(),
    axis.ticks = element_blank(),
    panel.grid.minor = element_blank(),
    panel.grid.major = element_blank(),
    panel.border = element_blank(),
    panel.background = element_blank(),
    #legend.position = "none"
  )

NA
NA

#rank GDP
data.top.hight <- data.gdp %>% select(country, year,GDP) %>%
  mutate(ranking = dense_rank(desc(GDP)))
data.top.hight

k <- 15
top.gdp <- data.top.hight %>% 
  #filter(ranking <= k + 1) %>% 
  arrange(ranking)
top.gdp <- head(top.gdp,21)
data.top.low <- data.gdp %>% select(country, year,GDP) %>%
  mutate(ranking = dense_rank(GDP))
low.gdp.long <- data.top.low %>% 
  #filter(ranking <= k + 1) %>% 
  arrange(ranking)
View(low.gdp.long)
low.gdp <- head(low.gdp.long,23)
low.gdp

#correlation
corr_data %<>% select(c(GDP,confirm.rate,death.rate,recover.rate,healthcare,avg_temp,Pornhub))
head(corr_data)

cor(corr_data)

                    GDP confirm.rate  death.rate recover.rate  healthcare    avg_temp
GDP           1.0000000    0.4350394 -0.23717777   -0.4763494  0.25183938  0.15986445
confirm.rate  0.4350394    1.0000000 -0.34002060   -0.7254358  0.46302123 -0.38053574
death.rate   -0.2371778   -0.3400206  1.00000000    0.1832321 -0.16021245  0.31063014
recover.rate -0.4763494   -0.7254358  0.18323215    1.0000000 -0.73011486  0.12398936
healthcare    0.2518394    0.4630212 -0.16021245   -0.7301149  1.00000000 -0.05087304
avg_temp      0.1598645   -0.3805357  0.31063014    0.1239894 -0.05087304  1.00000000
Pornhub       0.6997223    0.4557707 -0.07822766   -0.4581646  0.35166155  0.10674647
                 Pornhub
GDP           0.69972226
confirm.rate  0.45577066
death.rate   -0.07822766
recover.rate -0.45816457
healthcare    0.35166155
avg_temp      0.10674647
Pornhub       1.00000000

ggcorrplot(cor(corr_data),hc.order = TRUE,
           outline.color = "white",
           colors = c("#6D9EC1","white","#E46726"),
           lab = TRUE)

df <- data.long %>% filter(country %in% top.countries) %<>%
  mutate(country=country %>% factor(levels=c(top.countries)))
df %>% filter(country != 'World' & type != 'Total Confirmed') %>%
  ggplot(aes(x=date, y=count, fill=type)) +
  geom_area(alpha=0.5) +
# xlab('') + ylab('') +
  labs(title=paste0('Numbers of COVID-19 Cases in Top 20 Countries - ',
                    max.date.txt)) +
  scale_fill_manual(values=c('red', 'green', 'black')) +
  theme(legend.title=element_blank(), legend.position='bottom',
        plot.title = element_text(size=12),
        axis.title.x=element_blank(),
        axis.title.y=element_blank(),
        legend.key.size=unit(0.4, 'cm'),
        legend.text=element_text(size=12),
        strip.text.x=element_text(size=12),
        axis.text=element_text(size=12),
        axis.text.x=element_text(angle=45, hjust=1)) +
  facet_wrap(~country, ncol=4, scales='free_y')

p <- df %>% filter(country != 'World') %>%
  ggplot(aes(x=date, y=count, color=type)) +
  geom_line() +
  labs(title=paste0('Numbers of COVID-19 Cases in Top 20 Countries (log scale) - ',
                    max.date.txt)) +
  scale_color_manual(values=c('purple', 'red', 'green', 'black')) +
  theme(legend.title=element_blank(), legend.position='bottom',
        plot.title = element_text(size=10),
        axis.title.x=element_blank(),
        axis.title.y=element_blank(),
        legend.key.size=unit(0.4, 'cm'),
        legend.text=element_text(size=10),
        strip.text.x=element_text(size=10),
        axis.text=element_text(size=10),
        axis.text.x=element_text(angle=45, hjust=1)) +
  scale_y_continuous(trans='log10')
p + facet_wrap(~country, ncol=4, scales='free_y')

data.world %<>% arrange(desc(date)) %>%
  select(c(date, confirmed, deaths, recovered, current.confirmed,new.confirmed, new.deaths, new.recovered, rate.lower, rate.upper, rate.daily))
data.world %>%
  mutate(rate.upper = rate.upper %>% format(nsmall=1) %>% paste0('\\%'),
         rate.lower = rate.lower %>% format(nsmall=1) %>% paste0('\\%'),
         rate.daily = rate.daily %>% format(nsmall=1) %>% paste0('\\%')) 

#sars_2003
df_sars <- tbl(my_db, sql("select * from sars_2003_update"))
df_sars <- as.data.frame(df_sars)
df_sars

#datesSar <- as.Date(df_sars$Date,format = "%m/%d/%y")
#datesSar

df_sars %<>%  mutate(Date = as.Date(df_sars$Date,format = "%m/%d/%y"))
df_sars

## convert from wide to long format
dataSar.long <- df_sars %>%
  select(c(Date, country, Cumulative_number , Number_deaths, Number_recovered)) %>%
  gather(key=type, value=count, -c(country, Date))
## set factor levels to show them in a desirable order
dataSar.long %<>% mutate(type=recode_factor(type, Cumulative_number ='Cumulative Number',
                                         Number_deaths ='Number of deaths',
                                         Number_recovered ='Number of recovered'))
View(dataSar.long)
 

g <-
  ggplot(dataSar.long,aes(Date,count,color = type)) +
  geom_line()+
  geom_point()+
  xlab("")+
  ylab("")
g

NA

#Covid_Thailand
df_thai <- tbl(my_db, sql("select * from covid_Thailand"))
df_thai <- as.data.frame(df_thai)
df_thai

#clean Covid_Thailand
dates.th <- df_thai[,2]%>% mdy()
range(dates.th)

[1] "2020-01-12" "2021-01-15"

min.date.th <- min(dates.th)
max.date.th <- max(dates.th)
min.date.txt.th <- min.date.th %>% format('%d %b %Y')
max.date.txt.th <- max.date.th %>% format('%d %b %Y')

df_thai$announce_date <- mdy(df_thai$announce_date)
df_thai$notification_date <- mdy(df_thai$notification_date)
df_thai 

df_thai <- df_thai %>% select(!No.) %>% select(!notification_date) %>% 
  group_by(announce_date)
df_thai

# Total confirmed cases in Thailand
data.thai.count <- df_thai %>%
  select(announce_date) %>%
  summarise(comfirmed = n())  %>% as.data.frame()

`summarise()` ungrouping output (override with `.groups` argument)

data.thai.count$cumulative_confirmed <- cumsum(data.thai.count[, 2])
data.thai.count

## Thai Confirmed Cases (Jan 2020 - Jan 2021
plot1 <- ggplot(data.thai.count, aes(x=announce_date, y=cumulative_confirmed)) +
  geom_point() + geom_smooth() +
  xlab(" ") + ylab("Count") + labs(title='Thai Cumulative Confirmed Cases (Jan 2020 - Jan 2021)') +
  theme(axis.text.x=element_text(angle=45, hjust=1))
plot2 <- ggplot(data.thai.count, aes(x=announce_date, y=comfirmed)) +
  geom_point() + geom_smooth() +
  xlab(" ") + ylab("Count")+ labs(title='Thai Confirmed Cases (Jan 2020 - Jan 2021)') +
  theme(axis.text.x=element_text(angle=45, hjust=1))
## show two plots side by side
grid.arrange(plot1, plot2, ncol=1)

## Thai Confirmed Cases (Jan 2020 - Jan 2021) log scale
plot1 <- ggplot(data.thai.count, aes(x=announce_date, y=cumulative_confirmed)) +
  geom_point() + geom_smooth() +
  xlab(" ") + ylab("Count") + labs(title='Thai Cumulative Confirmed Cases (Jan 2020 - Jan 2021 log scale)') +
  theme(axis.text.x=element_text(angle=45, hjust=1))+scale_y_continuous(trans='log10')
plot2 <- ggplot(data.thai.count, aes(x=announce_date, y=comfirmed)) +
  geom_point() + geom_smooth() +
  xlab(" ") + ylab("Count")+ labs(title='Thai Confirmed Cases (Jan 2020 - Jan 2021 log scale)') +
  theme(axis.text.x=element_text(angle=45, hjust=1))+scale_y_continuous(trans='log10')
## show two plots side by side
grid.arrange(plot1, plot2, ncol=1)

# Confirmed cases divided by sex (gender)
data.thai.gender <- df_thai %>%
  group_by(sex) %>%
  summarise(count = n()) %>%
  mutate(percent = (count / sum(count) * 100) %>% round(2)) %>%
  filter(percent>1)%>%
  #mutate(pos = cumsum(percent) - 0.5*percent) %>%
  arrange(desc(percent))

`summarise()` ungrouping output (override with `.groups` argument)

data.thai.gender

NA

data.thai.gender$sex <- factor(data.thai.gender$sex, levels = as.character(data.thai.gender$sex))
data.thai.gender$sex

[1] Male   Female
Levels: Male Female

g.th.gender <- data.thai.gender %>% 
  ggplot(aes(x = "", y = percent, fill = sex)) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar("y") +
  theme_void() +
  labs(title='Gender of Thai Confirmed Cases (Jan 2020 - Jan 2021)')+
  geom_text(aes(label = paste0(percent, "%")), color = "white", size = 5, position = position_stack(vjust = 0.5)) +
  guides(fill = guide_legend(reverse = TRUE)) 
g.th.gender

# Confirmed cases divided by risk
data.thai.risk <- df_thai %>%
  group_by(risk) %>%
  summarise(count = n()) %>%
  arrange(desc(count))

`summarise()` ungrouping output (override with `.groups` argument)

data.thai.risk

# Confirmed cases divided by risk
data.thai.risk <- df_thai %>%
  group_by(risk) %>%
  summarise(count = n()) %>%
  mutate(percent = (count / sum(count) * 100) %>% round(2)) %>%
  filter(percent>3.5) %>%
  arrange(desc(percent)) 

`summarise()` ungrouping output (override with `.groups` argument)

data.thai.risk

data.thai.risk$risk[data.thai.risk$risk == "C"] <- "Close contact with the patient"
data.thai.risk$risk[data.thai.risk$risk == "F"] <- "State Quarantine"
data.thai.risk$risk[data.thai.risk$risk == "O"] <- "AOQ/ALQ/HQ/AHQ/OQ"
data.thai.risk$risk[data.thai.risk$risk == "G"] <- "Go to a crowded place"
data.thai.risk$risk[data.thai.risk$risk == "B"] <- "Thai people from abroad"
data.thai.risk$risk[data.thai.risk$risk == "D"] <- "Career at risk"
data.thai.risk$risk[data.thai.risk$risk == "H"] <- "Cabaret"
data.thai.risk

data.thai.risk$risk <- factor(data.thai.risk$risk, levels = as.character(data.thai.risk$risk))
data.thai.risk$risk

 [1] Close contact with the patient Cluster Samut Sakhon          
 [3] State Quarantine               Unknown                       
 [5] AOQ/ALQ/HQ/AHQ/OQ              Go to a crowded place         
 [7] Thai people from abroad        Cluster Rayong                
 [9] Career at risk                 Cabaret                       
10 Levels: Close contact with the patient Cluster Samut Sakhon State Quarantine ... Cabaret

g.th.risk <- data.thai.risk %>% 
  ggplot(aes(x = "", y = percent, fill = risk)) +
  geom_bar(stat = "identity", width = 0.5) +
  coord_polar("y") +
  theme_void() +
  labs(title='Risk of Thai Confirmed Cases(Jan 2020 - Jan 2021)')+
  geom_text(aes(label = paste0(percent, "%")), color = "Black", size = 3, position = position_stack(vjust = 0.5)) +
  guides(fill = guide_legend(reverse = TRUE)) 
g.th.risk

# Confirmed cases divided by age
data.thai.age <- df_thai %>%
  group_by(age,sex) %>% 
  filter(sex != "")%>%
  summarise(count = n()) %>%
  arrange(desc(count))

`summarise()` regrouping output by 'age' (override with `.groups` argument)

data.thai.age

ggplot(data.thai.age,aes(x=age,y=count,fill=sex))+geom_bar(stat = "identity")+
  labs(title='Age of Thai Confirmed Cases')+guides(fill=guide_legend(reverse = T))

# Confirmed cases divided by nationality
data.thai.nationality <- df_thai %>%
  group_by(nationality) %>%
  summarise(count = n()) %>%
  filter(count > 13)%>%
  arrange(desc(count))

`summarise()` ungrouping output (override with `.groups` argument)

#data.thai.nationality$nationality[nationality$nationality == "????????"] <- "Unknown"
data.thai.nationality

ggplot(data.thai.nationality,aes(x=nationality,y=count))+geom_bar(stat = "identity")+
  labs(title='Nationality of Thai Confirmed Cases')+coord_flip()